def init_global_params_from_bnpy_format(hmodel, Data, initname, prefix):
storedModel = ModelReader.load_model(initname, prefix)
# TODO check if dimension matches
aTypesMatch = type(storedModel.allocModel) == type(hmodel.allocModel)
oTypesMatch = type(storedModel.obsModel) == type(hmodel.obsModel)
inferTypesMatch = storedModel.inferType == hmodel.inferType
if aTypesMatch and oTypesMatch and inferTypesMatch:
hmodel.set_global_params(hmodel=storedModel)
else:
LP = storedModel.calc_local_params(Data)
SS = hmodel.get_global_suff_stats(Data, LP)
hmodel.update_global_params(SS)
def init_global_params_from_bnpy_format(hmodel, Data, initname, prefix):
storedModel = ModelReader.load_model(initname, prefix)
# TODO check if dimension matches
aTypesMatch = type(storedModel.allocModel) == type(hmodel.allocModel)
oTypesMatch = type(storedModel.obsModel) == type(hmodel.obsModel)
inferTypesMatch = storedModel.inferType == hmodel.inferType
if aTypesMatch and oTypesMatch and inferTypesMatch:
hmodel.set_global_params(hmodel=storedModel)
else:
LP = storedModel.calc_local_params(Data)
SS = hmodel.get_global_suff_stats(Data, LP)
hmodel.update_global_params(SS)
def test_save_then_load_same_model(self, prefix='Test'):
''' Verify that we can save model H to disk, load it back in as G, and
get same results from calculations on H,G with same inputs
'''
ModelWriter.save_model(self.hmodel, '/tmp/', prefix)
gmodel = ModelReader.load_model('/tmp/', prefix)
assert type(gmodel.allocModel) == type(self.hmodel.allocModel)
assert gmodel.allocModel.K == self.hmodel.allocModel.K
K = gmodel.allocModel.K
for k in range(K):
gSig = gmodel.obsModel.comp[k].ECovMat()
hSig = self.hmodel.obsModel.comp[k].ECovMat()
assert np.allclose(gSig, hSig)
# Make sure we get same responsibilities before and after
hLP = self.hmodel.calc_local_params(self.Data)
gLP = gmodel.calc_local_params(self.Data)
assert np.allclose(hLP['resp'], gLP['resp'])
def main():
args = parse_args()
jobpath, taskids = parse_jobpath_and_taskids(args)
for taskid in taskids:
taskpath = os.path.join(jobpath, taskid)
if args.lap is not None:
prefix, bLap = ModelReader.getPrefixForLapQuery(taskpath, args.lap)
if bLap != args.lap:
print 'Using saved lap: ', bLap
else:
prefix = 'Best' # default
hmodel = ModelReader.load_model(taskpath, prefix)
plotModelInNewFigure(jobpath, hmodel, args)
if args.savefilename is not None:
pylab.show(block=False)
pylab.savefig(args.savefilename % (taskid))
if args.savefilename is None:
pylab.show(block=True)
def init_global_params_from_bnpy_format(hmodel,
Data,
initname,
initLapFrac=-1,
prefix='Best',
**kwargs):
""" Initialize global parameters for hmodel from bnpy disk format.
Post Condition
-------
hmodel has valid global parameters.
'''
"""
if initLapFrac > -1:
storedModel, lap = ModelReader.loadModelForLap(initname, initLapFrac)
else:
storedModel = ModelReader.load_model(initname, prefix)
try:
hmodel.set_global_params(hmodel=storedModel,
obsModel=storedModel.obsModel)
except AttributeError:
LP = storedModel.calc_local_params(Data)
SS = hmodel.get_global_suff_stats(Data, LP)
hmodel.update_global_params(SS)